Download [SURVEY OF]VISUALIZATION [TECHNIQUES] IN PROCESS

[SURVEY OF]VISUALIZATION [TECHNIQUES]
IN PROCESS MINING
M. Amiyo1, W.M.P. van der Aalst2, and P. J. Ogao1
1. Makerere University
P. O. Box 7062, Kampala, Uganda
{mamiyo, [email protected]}
2. Technische Universiteit Eindhoven
PO Box 513, 5600 MB Eindhoven, The Netherlands
[email protected]
Abstract: Visualization of process mining results is a very important for one to
get a good understanding of a business process. This becomes more crucial as
process become more complex. Unfortunately, the methods currently used to
visualize these results do not put into consideration automatic processing feature.
As a result, as the complexity of the processes increase, the complexity and size of
a resulting model increase and so does the difficulty in interpretation. This paper
seeks to review the visualization techniques that are being used to present process
mining results and to evaluate their effectiveness.
1.
INTRODUCTION
Business companies or organizations are always looking for ways in which they can improve on
their business so that they can have a competitive advantage. In this Dot.com age, most
organizations have information systems to support their daily activities, making them more
efficient. In addition, companies have invested in finding hidden knowledge from the
information they already have through data mining techniques. These techniques are used to
identify patterns within, a usually large, data set. This knowledge is useful in for a number of
things which include decision making. However, the current trend shows that there has been shift
from the data-driven or data oriented information systems to process-driven or process oriented
information systems (Van der Aalst, 2004).
As a way to understand business processes, the research field of process mining was born. The
development and increased use of process mining techniques was and is based on the data
mining techniques (Turner and Tiwari, 2008). Whereas data mining focuses on getting or
extracting knowledge from large sets of data, Process mining focuses on extracting knowledge
from data logs that are recorded during the execution of process (Turner and Tiwari, 2008), by
the information system.
Process mining can be done for three major reasons; discovery, conformance check and
extension of process models. Therefore it may be defined as a method of extracting a structured
description of a process from a set of process logs, recorded during process execution. This
description is a model that gives insight to the behaviour of a process according to different
perspectives (Van der Aalst, 2004). The mined models are generated based on a process
modelling language. Some of the languages used include Petri Nets and Event-driven Process
Chains, which are known as graphical business process modelling languages thus can be used to
understand and give a visual interpretation of a process (Streit, Pham, and Brown, 2005). The
behaviour of a process may also be understood by distilling the process logs in light of several
perspectives that can be viewed or considered in combination and with interesting aspects such
as the time, and location. (Van der Aalst, 2004).
Empowering users in organizations with the ability to understand the behaviour of processes has
lead to the increased use of process mining. Regardless of this, visualization of process mining
results is still a challenge (Van der Aalst, 2004). As the complexity of a business process
increases, the resulting graph or mined model becomes bigger and more difficult to interpret
(Streit, Pham, and Brown, 2005) i.e., “Petri Net-based models tend to become too large for
analysis even for a modest-size system” (Murata, 1999 pp 542). Techniques such as zooming
(Streit, Pham, and Brown, 2005) and abstraction (Murata, 1999 pp 542) that have been used in
attempt to overcome this problem have limitations. In addition to this, features such as colour
that could be used to carry additional information, find limited use in most graphical business
process languages (Streit, Pham, and Brown, 2005). For example, in most business process
modelling languages (BPML), controlled visual processing features are more dominant as
compare to automatic processing features such as colour (Streit, Pham, and Brown, 2005) which
is a powerful tool in visualization. EPC, an example of a BPML language, contains some
automatic processing features like colour; however the colour does not additional information
that can be used in the interpretation of the mined process models (Streit, Pham, and Brown,
2005).
In light of these, it is clear that the presentation of a mined model is very critical to
understanding the behaviour of a process, especially large complex processes. In order for large
mined models to be understood, the level of controlled visual processing features should be
reduced (Streit, Pham, and Brown, 2005). This leaves a need to find appropriate visualization
techniques that can be used to help in the visualization of complex process models.
This paper seeks to review the visualization techniques used in the presentation of process
mining results. This is done with an aim of evaluating their effectiveness in presentation of
complex processes. The rest of the paper is organized as follows; the next section discusses key
concepts and definitions, section 3. A review of visualization techniques used in process mining,
section 4 gives an evaluation of the techniques identified in section 3 and section 5 and 6 give a
discussion and concluding remarks.
2.
KEY CONCEPTS AND DEFINITIONS
PROCESS MINING
In the past, information gathered from in the enactment stage in the life cycle of a business
process was rarely used except for security and audit purposes. However, today these event logs
can be used to analyze and understand the underlying business process through process mining.
Process mining may be defined as method of extracting a structured description of a process
from a set of process logs, recorded during process execution (Van der Aalst, 2004). This makes
process mining an important means by which processes can be analyzed and monitored. The
types of process mining include process discovery, conformance checking and process extension.
Process mining therefore gives as insight to what is actually going on within an organisation as
compared to what is expected (Van der Aalst, 2008).
The information extracted from such event (process) logs and mainly in form process models.
The business process model consists of an ordered set of activities that take place in a given
process showing the relationships/interactions between then, thus describing the process. These
models are presented in various business process modelling languages notations for example the
Event-driven Process chains (EPC) and Petri Nets.
Process mining results
Process mining results are mainly in form of a process model based on a given process modelling
language notation. Different process modelling languages have different notations but all have
four basic concepts; timepoint, activity, event and state. The time point represents the instant of
time that is not decomposable, activity; some kind of performance, event; a note worthy
occurrence and state; a set of properties of something, (Sӧderstrӧm, Andersson, Johannesson,
Perjons and Wangter, 2002). However they all perform the same purpose of describing what a
given process does and the objects and information it works on, (Xinming and Haikun, 2005).
The results process models show one of many perspectives. These perspectives include; the
functional, behavioural, organizational and informational. This basically means that process
models are perspective oriented; a user’s the view of interest determines the perspective of a
model and thus what the model can be used for.
In process mining, it is possible to discover models depending on three perspectives; case
(data/informational) perspective, process (functional) perspective and organizational
(behavioural) perspectives (Weijters, Van der Aalst, B. van Dongen, Günther, Mans, Alves de
Medeiros, Rozinat, Song, and Verbeek, 2007). It is therefore the nature of all models to be
incomplete and to focus on simplified specific views or perspectives for a particular purpose
(Xinming and Haikun, 2005).
The purpose of process mining results may be summarized into two broad categories; to inform
and to aid action. As a means to Inform, process mining results provide a manager and/or
decision makers with information about the process; what is done with this information is totally
left to the intended recipient. When the purpose is to aid in action, the outcome of the findings
must be used to determine next line of action (Van der Aalst, 2006).
Process Mining Framework (ProM)
This is an open source framework, providing an environment for performing different types of
process mining for example, process discovery and conformance checking. It supports process
mining by providing a number of plug-ins based on different techniques that can be used to
extract information from event logs. ProM also supports a number of process modelling language
notations which gives the user the ability to have various presentations of a given model. The
ProM environment is a versatile and extendible one i.e. plug-ins can be developed and easily
incorporated into the framework (Van Dongen et al., 2005)
VISUALIZATION
Visualization is a means by which abstract information, may be in form of text and numbers, is
represented in a more graphical manner for easy communication or interpretation. It can
therefore be defined as is any technique for creating images, diagrams, or animations to
communicate a message; giving insight to the meaning of data (Streit, Pham, and Brown, 2005).
The main purposed of visualization is providing insight into complex scenarios. It mainly
focuses on finding meaning in unexplored data for instance, in this process mining we look at the
event logs as unexplored data. Visualization avails users with graphical representations and
animations techniques that have been used in several research fields for identifying data
problems, provide insight and show relationships within a given dataset.
There are a number major categories of visualization methods under which there are several
techniques. These methods include; data, information, concept, strategy, metaphor and
compound visualization.
3.
VISUALISATION TECHNIQUES
There are many/several visualization techniques that have been developed over the years. This
section gives a review of the visualization techniques that are currently being used in presenting
process mining results.
There are a number of visualization technique concepts that have been used in presentation of
process mining results include; matrices, clustering, graph visualization and a combination of
these.
Graph Visualization
This is a technique that is used to display data that has elements that have a relation between
them. Data presentation is characterized by nodes and edges; the nodes representing the data
elements and the edges represent the relation between them.
Graph visualization has been applied in various domains including real-time systems represented
as state-transition systems or Petri nets (Herman et al., 2000). In process mining, process models
may be constructed as Petri nets or EPCs. As Petri nets, the data elements can take either of two
kinds of nodes; a place or a transition and the relation between them is represented as directed
edges.
Each graph has a layout associated to it. This determines the way the nodes and edges appear or
are displayed. These are usually presented or implemented as algorithms. A summery of the
different kinds of graph layouts is summarized in the figure below.
Figure 1: Overview of Graph layout algorithms (Herman et al., 2000)
The most commonly used graph layout in process mining is the tree layout.
Matrix Visualization
This involves the presentation of data inform of a matrix (rows and columns in a matrix) and
converting this data matrix to a matrix map. Each data entry is represented as by a dot or square
of a given colour (Legát, 2005).
This has been used in the dotted chat and the cloud chamber. Some of the performance analysis
results are displayed using this technique.
Clustering
Clustering is a process of grouping data or discovering classes of data based on a number of
conditions or rules. Clustering is a concept used in visualization to group related data, as a means
to reduce on complexity and space being utilized to display a large graph (Herman et al., 2000).
This technique has not been used independently in process mining results. It has been used to
clusters related tasks or events in EPC process model diagrams for example it is has been used in
conjunction with the fuzzy miner.
4.
EVALUATION OF TECHNIQUES
This section will contain an evaluation of the challenges or loop holes associated with the
visualization techniques identified in the section above in relation to process mining results.
5.
CONCLUSION
This section will contain concluding remarks and possible recommendations to address the
challenges or loop holes identified in the section above.
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